Face Classification Using Color Information

Atul Sajjanhar,Ahmed Mohammed

doi:10.3390/info8040155

Abstract

Color models are widely used in image recognition because they represent significant information. On the other hand, texture analysis techniques have been extensively used for facial feature extraction. In this paper; we extract discriminative features related to facial attributes by utilizing different color models and texture analysis techniques. Specifically, we propose novel methods for texture analysis to improve classification performance of race and gender. The proposed methods for texture analysis are based on Local Binary Pattern and its derivatives. These texture analysis methods are evaluated for six color models (hue, saturation and intensity value (HSV); L*a*b*; RGB; YCbCr; YIQ; YUV) to investigate the effect of each color model. Further, we configure two combinations of color channels to represent color information suitable for gender and race classification of face images. We perform experiments on publicly available face databases. Experimental results show that the proposed approaches are effective for the classification of gender and race.

Highlights

Face images contain information which is useful for face classification [1]
We propose novel texture analysis approaches for face classification and test these approaches for different color models
We evaluate the proposed texture analysis methods for classification of face images obtained from the FERET database [25]

Summary

Introduction

Face images contain information which is useful for face classification [1]. Face may be classified on the basis of race, gender, age, or expression. Face classification has attracted the attention of many researchers due to a wide range of applications such as security, surveillance, identity verification and video indexing [2,3,4,5]. The human face image is rich with demographic information that can be utilized to classify face images . Gender and race attributes are used for face classification. Demographic prediction of gender and race has been previously studied. Han and Jain [6] and Han et al [1] classified face images according to demographic information using biologically inspired features (BIF).

Methods

Results

Discussion

Conclusion